{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Node Representation Learning with attri2vec on Citeseer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the python implementation of the attri2vec algorithm outlined in paper ***[Attributed Network Embedding Via Subspace Discovery](https://arxiv.org/abs/1901.04095)*** D. Zhang, Y. Jie, X. Zhu and C. Zhang, arXiv:1901.04095, [cs.SI], 2019. The implementation uses the stellargraph libraries.\n",
    "\n",
    "\n",
    "## attri2vec\n",
    "\n",
    "attri2vec learns node representations by performing a linear/non-linear mapping on node content attributes. To make the learned node representations respect structural similarity, [`DeepWalk`](https://dl.acm.org/citation.cfm?id=2623732)/[`node2vec`](https://snap.stanford.edu/node2vec) learning mechanism is used to make nodes sharing similar random walk context nodes represented closely in the subspace, which is achieved by maximizing the occurrence probability of context nodes conditioned on the representation of the target nodes. The probability is modelled by Softmax and negative sampling is used to speed up its calculation. This makes attri2vec equivalent to predict whether a node occurs in the given target node's context in random walks with the representation of the target node, by minimizing the cross-entropy loss. \n",
    "\n",
    "In implementation, node embeddings are learnt by solving a simple classification task: given a large set of \"positive\" `(target, context)` node pairs generated from random walks performed on the graph (i.e., node pairs that co-occur within a certain context window in random walks), and an equally large set of \"negative\" node pairs that are randomly selected from the graph according to a certain distribution, learn a binary classifier that predicts whether arbitrary node pairs are likely to co-occur in a random walk performed on the graph. Through learning this simple binary node-pair-classification task, the model automatically learns an inductive mapping from attributes of nodes to node embeddings in a low-dimensional vector space, which preserves structural and feature similarities of the nodes. \n",
    "\n",
    "To train the attri2vec model, we first construct a training set of nodes, which is composed of an equal number of positive and negative `(target, context)` pairs from the graph. The positive `(target, context)` pairs are the node pairs co-occurring on random walks over the graph whereas the negative node pairs are the sampled randomly from the global node degree distribution of the graph. In attri2vec, each node is attached with two kinds of embeddings: 1) the inductive 'input embedding', i.e, the objective embedding, obtained by perform a linear/non-linear transformation on node content features, and 2) 'output embedding', i.e., the parameter vector used to predict its occurrence as a context node, obtained by looking up a parameter table. Given a `(target, context)` pair, attri2vec outputs a predictive value to indicate whether it is positive or negative, which is obtained by performing the dot product of the 'input embedding' of the target node and the 'output embedding' of the context node, followed by a sigmoid activation. \n",
    "\n",
    "The entire model is trained end-to-end by minimizing the binary cross-entropy loss function with regards to predicted node pair labels and true node pair labels, using stochastic gradient descent (SGD) updates of the model parameters, with minibatches of 'training' node pairs generated on demand and fed into the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import random\n",
    "\n",
    "import stellargraph as sg\n",
    "from stellargraph.data import UnsupervisedSampler\n",
    "from stellargraph.mapper import Attri2VecLinkGenerator, Attri2VecNodeGenerator\n",
    "from stellargraph.layer import Attri2Vec, link_classification\n",
    "\n",
    "from tensorflow import keras\n",
    "\n",
    "from pandas.core.indexes.base import Index\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "from sklearn.metrics import accuracy_score\n",
    "from stellargraph import datasets\n",
    "from IPython.display import display, HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "The CiteSeer dataset consists of 3312 scientific publications classified into one of six classes. The citation network consists of 4732 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 3703 unique words."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = datasets.CiteSeer()\n",
    "display(HTML(dataset.description))\n",
    "dataset.download()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load edges in order 'cited-paper' <- 'citing-paper'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "citeseer_location = os.path.join(dataset.data_directory, \"citeseer.cites\")\n",
    "g_nx = nx.read_edgelist(path=citeseer_location, create_using=nx.DiGraph()).reverse()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the graph to undirected graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "g_nx = g_nx.to_undirected()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the node attribute data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "citeseer_data_location = os.path.join(dataset.data_directory, \"citeseer.content\")\n",
    "attr_names = [\"w_{}\".format(ii) for ii in range(3703)]\n",
    "node_column_names = attr_names + [\"subject\"]\n",
    "node_attr = pd.read_csv(\n",
    "    citeseer_data_location, sep=\"\\t\", header=None, names=node_column_names\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change the type of the indexes of node_attr to str."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_attr.index = Index(list(map(str, list(node_attr.index))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The original graph contains some nodes with no attributes. We remove them here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "g_nx = g_nx.subgraph(list(node_attr.index))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select the largest connected component. For clarity we ignore isolated nodes and subgraphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Largest subgraph statistics: 2110 nodes, 3720 edges\n"
     ]
    }
   ],
   "source": [
    "g_nx_ccs = (g_nx.subgraph(c).copy() for c in nx.connected_components(g_nx))\n",
    "g_nx = max(g_nx_ccs, key=len)\n",
    "print(\n",
    "    \"Largest subgraph statistics: {} nodes, {} edges\".format(\n",
    "        g_nx.number_of_nodes(), g_nx.number_of_edges()\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Specify node and edge types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx.set_node_attributes(g_nx, \"paper\", \"label\")\n",
    "nx.set_edge_attributes(g_nx, \"cites\", \"label\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the ids of the nodes in the selected largest connected component. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_ids = sorted(list(g_nx.nodes))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get node features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_features = node_attr[attr_names].reindex(node_ids)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the Stellargraph with node features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = sg.StellarGraph(g_nx, node_features=node_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NetworkXStellarGraph: Undirected multigraph\n",
      " Nodes: 2110, Edges: 3720\n",
      "\n",
      " Node types:\n",
      "  paper: [2110]\n",
      "    Edge types: paper-cites->paper\n",
      "\n",
      " Edge types:\n",
      "    paper-cites->paper: [3720]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(G.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train attri2vec on Citeseer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Specify the other optional parameter values: root nodes, the number of walks to take per node, the length of each walk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes = list(G.nodes())\n",
    "number_of_walks = 4\n",
    "length = 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the UnsupervisedSampler instance with the relevant parameters passed to it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "unsupervised_samples = UnsupervisedSampler(\n",
    "    G, nodes=nodes, length=length, number_of_walks=number_of_walks\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the batch size and the number of epochs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 50\n",
    "epochs = 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define an attri2vec generator, which generates batches of (target, context) nodes and labels for the node pair."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = Attri2VecLinkGenerator(G, batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Building the model: a 1-hidden-layer node representation ('input embedding') of the `target` node and the parameter vector ('output embedding') for predicting the existence of `context node` for each `(target context)` pair, with a link classification layer performed on the dot product of the 'input embedding' of the `target` node and the 'output embedding' of the `context` node.\n",
    "\n",
    "Attri2Vec part of the model, with a 128-dimenssion hidden layer, no bias term and no normalization. (Normalization can be set to 'l2'). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "layer_sizes = [128]\n",
    "attri2vec = Attri2Vec(\n",
    "    layer_sizes=layer_sizes, generator=generator, bias=False, normalize=None\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build the model and expose input and output sockets of attri2vec, for node pair inputs:\n",
    "x_inp, x_out = attri2vec.build()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the link_classification function to generate the prediction, with the 'ip' edge embedding generation method and the 'sigmoid' activation, which actually performs the dot product of the 'input embedding' of the target node and the 'output embedding' of the context node followed by a sigmoid activation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "link_classification: using 'ip' method to combine node embeddings into edge embeddings\n"
     ]
    }
   ],
   "source": [
    "prediction = link_classification(\n",
    "    output_dim=1, output_act=\"sigmoid\", edge_embedding_method=\"ip\"\n",
    ")(x_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stack the Attri2Vec encoder and prediction layer into a Keras model, and specify the loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Model(inputs=x_inp, outputs=prediction)\n",
    "\n",
    "model.compile(\n",
    "    optimizer=keras.optimizers.Adam(lr=1e-3),\n",
    "    loss=keras.losses.binary_crossentropy,\n",
    "    metrics=[keras.metrics.binary_accuracy],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/4\n",
      "1351/1351 - 29s - loss: 0.6829 - binary_accuracy: 0.5566\n",
      "Epoch 2/4\n",
      "1351/1351 - 28s - loss: 0.5492 - binary_accuracy: 0.7208\n",
      "Epoch 3/4\n",
      "1351/1351 - 28s - loss: 0.3677 - binary_accuracy: 0.8615\n",
      "Epoch 4/4\n",
      "1351/1351 - 29s - loss: 0.2686 - binary_accuracy: 0.9097\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(\n",
    "    generator.flow(unsupervised_samples),\n",
    "    epochs=epochs,\n",
    "    verbose=2,\n",
    "    use_multiprocessing=False,\n",
    "    workers=1,\n",
    "    shuffle=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Visualise Node Embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build the node based model for predicting node representations from node content attributes with the learned parameters. Below a Keras model is constructed, with x_inp[0] as input and x_out[0] as output. Note that this model's weights are the same as those of the corresponding node encoder in the previously trained node pair classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_inp_src = x_inp[0]\n",
    "x_out_src = x_out[0]\n",
    "embedding_model = keras.Model(inputs=x_inp_src, outputs=x_out_src)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the node embeddings by applying the learned mapping function to node content features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "43/43 [==============================] - 0s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "node_gen = Attri2VecNodeGenerator(G, batch_size).flow(node_ids)\n",
    "node_embeddings = embedding_model.predict_generator(node_gen, workers=1, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get node subjects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_targets = [node_attr[\"subject\"][node_id] for node_id in node_ids]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transform the embeddings to 2d space for visualisation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform = TSNE  # PCA\n",
    "\n",
    "trans = transform(n_components=2)\n",
    "node_embeddings_2d = trans.fit_transform(node_embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw the embedding points, coloring them by the target label (paper subject)\n",
    "alpha = 0.7\n",
    "label_map = {l: i for i, l in enumerate(np.unique(node_targets))}\n",
    "node_colours = [label_map[target] for target in node_targets]\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.axes().set(aspect=\"equal\")\n",
    "plt.scatter(\n",
    "    node_embeddings_2d[:, 0],\n",
    "    node_embeddings_2d[:, 1],\n",
    "    c=node_colours,\n",
    "    cmap=\"jet\",\n",
    "    alpha=alpha,\n",
    ")\n",
    "plt.title(\"{} visualization of node embeddings\".format(transform.__name__))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Node Classificaion Task"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The embeddings learned by `attri2vec` can be used as feature vectors in downstream tasks, such as node classification and link prediction.\n",
    "\n",
    "In this example, we will use the `attri2vec` node embeddings to train a classifier to predict the subject of a paper in DBLP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X will hold the 128-dimensional input features\n",
    "X = node_embeddings\n",
    "# y holds the corresponding target values\n",
    "y = np.array(node_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Splitting\n",
    "\n",
    "We split the data into train and test sets. \n",
    "\n",
    "We use 10% of the data for training and the remaining 90% for testing as a hold out test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array shapes:\n",
      " X_train = (211, 128)\n",
      " y_train = (211,)\n",
      " X_test = (1899, 128)\n",
      " y_test = (1899,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.1, test_size=None)\n",
    "print(\n",
    "    \"Array shapes:\\n X_train = {}\\n y_train = {}\\n X_test = {}\\n y_test = {}\".format(\n",
    "        X_train.shape, y_train.shape, X_test.shape, y_test.shape\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classifier Training\n",
    "\n",
    "We train a Logistic Regression classifier on the training data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegressionCV(Cs=10, class_weight=None, cv=10, dual=False,\n",
       "                     fit_intercept=True, intercept_scaling=1.0, l1_ratios=None,\n",
       "                     max_iter=300, multi_class='ovr', n_jobs=None, penalty='l2',\n",
       "                     random_state=None, refit=True, scoring='accuracy',\n",
       "                     solver='lbfgs', tol=0.0001, verbose=False)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = LogisticRegressionCV(\n",
    "    Cs=10, cv=10, scoring=\"accuracy\", verbose=False, multi_class=\"ovr\", max_iter=300\n",
    ")\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predict the hold-out test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the accuracy of the classifier on the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7498683517640864"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
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